Tabular Data classification

Tabular data classification is a type of machine learning problem.
Each example consists of a list of features, each of which is either numeric or categorical:
  • Numeric features are integer or floating-point numbers.
    • Examples:
      • [1, 5, -2, 3,...]
      • [20.1, 12.5, -2.2, ...]
  • Categorical features are values from a finite list of options.
    • Examples:
      • ['dog', 'cat', 'cat', 'dog', ...] (options: {'dog', 'cat'})
      • [0, 1, 2, 0, ..] (options: {0, 1, 2})
    • Note that categorical features can take integer values, as in the second example above. Unlike in the case of a number feature, however, the order of the numbers is not meaningful.
The task is to predict a class label from a finite list of possible classes, using the information in the features.

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